{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# diagnosis\n",
    "\n",
    "The diagnosis table is used to track active problems for a given patient. It contains a list of diagnoses that were documented for each patient in the Active Diagnosis/Treatment sections of the eCareManager medical record. For some records, the corresponding International Classification of Diseases (ICD) codes are available. This can be useful for determining if certain diseases were documented during the ICU stay and at what point in the patient’s ICU stay these diagnoses were documented.\n",
    "\n",
    "Important columns in this table include:\n",
    "\n",
    "* `diagnosisstring` - the problem documented\n",
    "* `icd9code` - a mapping from the problem to ICD-9 **and** ICD-10 code (the name of the column is antiquated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alistairewj/.local/lib/python3.5/site-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use \"pip install psycopg2-binary\" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.\n",
      "  \"\"\")\n"
     ]
    }
   ],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import psycopg2\n",
    "import getpass\n",
    "import pdvega\n",
    "\n",
    "# for configuring connection \n",
    "from configobj import ConfigObj\n",
    "import os\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database: eicu\n",
      "Username: alistairewj\n"
     ]
    }
   ],
   "source": [
    "# Create a database connection using settings from config file\n",
    "config='../db/config.ini'\n",
    "\n",
    "# connection info\n",
    "conn_info = dict()\n",
    "if os.path.isfile(config):\n",
    "    config = ConfigObj(config)\n",
    "    conn_info[\"sqluser\"] = config['username']\n",
    "    conn_info[\"sqlpass\"] = config['password']\n",
    "    conn_info[\"sqlhost\"] = config['host']\n",
    "    conn_info[\"sqlport\"] = config['port']\n",
    "    conn_info[\"dbname\"] = config['dbname']\n",
    "    conn_info[\"schema_name\"] = config['schema_name']\n",
    "else:\n",
    "    conn_info[\"sqluser\"] = 'postgres'\n",
    "    conn_info[\"sqlpass\"] = ''\n",
    "    conn_info[\"sqlhost\"] = 'localhost'\n",
    "    conn_info[\"sqlport\"] = 5432\n",
    "    conn_info[\"dbname\"] = 'eicu'\n",
    "    conn_info[\"schema_name\"] = 'public,eicu_crd'\n",
    "    \n",
    "# Connect to the eICU database\n",
    "print('Database: {}'.format(conn_info['dbname']))\n",
    "print('Username: {}'.format(conn_info[\"sqluser\"]))\n",
    "if conn_info[\"sqlpass\"] == '':\n",
    "    # try connecting without password, i.e. peer or OS authentication\n",
    "    try:\n",
    "        if (conn_info[\"sqlhost\"] == 'localhost') & (conn_info[\"sqlport\"]=='5432'):\n",
    "            con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                                   user=conn_info[\"sqluser\"])            \n",
    "        else:\n",
    "            con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                                   host=conn_info[\"sqlhost\"],\n",
    "                                   port=conn_info[\"sqlport\"],\n",
    "                                   user=conn_info[\"sqluser\"])\n",
    "    except:\n",
    "        conn_info[\"sqlpass\"] = getpass.getpass('Password: ')\n",
    "\n",
    "        con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                               host=conn_info[\"sqlhost\"],\n",
    "                               port=conn_info[\"sqlport\"],\n",
    "                               user=conn_info[\"sqluser\"],\n",
    "                               password=conn_info[\"sqlpass\"])\n",
    "query_schema = 'set search_path to ' + conn_info['schema_name'] + ';'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine a single patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "patientunitstayid = 2704494"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patientunitstayid</th>\n",
       "      <th>activeupondischarge</th>\n",
       "      <th>diagnosisid</th>\n",
       "      <th>diagnosisyear</th>\n",
       "      <th>diagnosistime24</th>\n",
       "      <th>diagnosistime</th>\n",
       "      <th>diagnosisoffset</th>\n",
       "      <th>diagnosisstring</th>\n",
       "      <th>icd9code</th>\n",
       "      <th>diagnosispriority</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2704494</td>\n",
       "      <td>False</td>\n",
       "      <td>32722297</td>\n",
       "      <td>2014</td>\n",
       "      <td>05:13:31</td>\n",
       "      <td>morning</td>\n",
       "      <td>48</td>\n",
       "      <td>renal|disorder of kidney|acute renal failure</td>\n",
       "      <td>584.9, N17.9</td>\n",
       "      <td>Major</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2704494</td>\n",
       "      <td>False</td>\n",
       "      <td>31935854</td>\n",
       "      <td>2014</td>\n",
       "      <td>05:13:31</td>\n",
       "      <td>morning</td>\n",
       "      <td>48</td>\n",
       "      <td>oncology|chest tumors|breast CA|female</td>\n",
       "      <td>174.9, C50.919</td>\n",
       "      <td>Major</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2704494</td>\n",
       "      <td>False</td>\n",
       "      <td>33818792</td>\n",
       "      <td>2014</td>\n",
       "      <td>05:13:31</td>\n",
       "      <td>morning</td>\n",
       "      <td>48</td>\n",
       "      <td>cardiovascular|shock / hypotension|hypotension</td>\n",
       "      <td>458.9, I95.9</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2704494</td>\n",
       "      <td>False</td>\n",
       "      <td>32391279</td>\n",
       "      <td>2014</td>\n",
       "      <td>05:13:31</td>\n",
       "      <td>morning</td>\n",
       "      <td>48</td>\n",
       "      <td>oncology|hematologic malignancy|leukemia</td>\n",
       "      <td></td>\n",
       "      <td>Major</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2704494</td>\n",
       "      <td>False</td>\n",
       "      <td>33010439</td>\n",
       "      <td>2014</td>\n",
       "      <td>16:18:46</td>\n",
       "      <td>evening</td>\n",
       "      <td>2153</td>\n",
       "      <td>renal|disorder of kidney|acute renal failure</td>\n",
       "      <td>584.9, N17.9</td>\n",
       "      <td>Major</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2704494</td>\n",
       "      <td>False</td>\n",
       "      <td>33098216</td>\n",
       "      <td>2014</td>\n",
       "      <td>16:18:46</td>\n",
       "      <td>evening</td>\n",
       "      <td>2153</td>\n",
       "      <td>cardiovascular|shock / hypotension|hypotension</td>\n",
       "      <td>458.9, I95.9</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2704494</td>\n",
       "      <td>False</td>\n",
       "      <td>32825172</td>\n",
       "      <td>2014</td>\n",
       "      <td>16:18:46</td>\n",
       "      <td>evening</td>\n",
       "      <td>2153</td>\n",
       "      <td>oncology|chest tumors|breast CA|female</td>\n",
       "      <td>174.9, C50.919</td>\n",
       "      <td>Major</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2704494</td>\n",
       "      <td>False</td>\n",
       "      <td>33368560</td>\n",
       "      <td>2014</td>\n",
       "      <td>16:18:46</td>\n",
       "      <td>evening</td>\n",
       "      <td>2153</td>\n",
       "      <td>oncology|hematologic malignancy|leukemia</td>\n",
       "      <td></td>\n",
       "      <td>Major</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2704494</td>\n",
       "      <td>True</td>\n",
       "      <td>33376417</td>\n",
       "      <td>2014</td>\n",
       "      <td>16:24:51</td>\n",
       "      <td>evening</td>\n",
       "      <td>2159</td>\n",
       "      <td>cardiovascular|shock / hypotension|hypotension</td>\n",
       "      <td>458.9, I95.9</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2704494</td>\n",
       "      <td>True</td>\n",
       "      <td>32981160</td>\n",
       "      <td>2014</td>\n",
       "      <td>16:24:51</td>\n",
       "      <td>evening</td>\n",
       "      <td>2159</td>\n",
       "      <td>oncology|chest tumors|breast CA|female</td>\n",
       "      <td>174.9, C50.919</td>\n",
       "      <td>Major</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2704494</td>\n",
       "      <td>True</td>\n",
       "      <td>33529588</td>\n",
       "      <td>2014</td>\n",
       "      <td>16:24:51</td>\n",
       "      <td>evening</td>\n",
       "      <td>2159</td>\n",
       "      <td>renal|disorder of kidney|acute renal failure</td>\n",
       "      <td>584.9, N17.9</td>\n",
       "      <td>Major</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2704494</td>\n",
       "      <td>True</td>\n",
       "      <td>32465222</td>\n",
       "      <td>2014</td>\n",
       "      <td>16:24:51</td>\n",
       "      <td>evening</td>\n",
       "      <td>2159</td>\n",
       "      <td>oncology|hematologic malignancy|leukemia</td>\n",
       "      <td></td>\n",
       "      <td>Major</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    patientunitstayid activeupondischarge  diagnosisid  diagnosisyear  \\\n",
       "0             2704494               False     32722297           2014   \n",
       "1             2704494               False     31935854           2014   \n",
       "2             2704494               False     33818792           2014   \n",
       "3             2704494               False     32391279           2014   \n",
       "4             2704494               False     33010439           2014   \n",
       "5             2704494               False     33098216           2014   \n",
       "6             2704494               False     32825172           2014   \n",
       "7             2704494               False     33368560           2014   \n",
       "8             2704494                True     33376417           2014   \n",
       "9             2704494                True     32981160           2014   \n",
       "10            2704494                True     33529588           2014   \n",
       "11            2704494                True     32465222           2014   \n",
       "\n",
       "   diagnosistime24 diagnosistime  diagnosisoffset  \\\n",
       "0         05:13:31       morning               48   \n",
       "1         05:13:31       morning               48   \n",
       "2         05:13:31       morning               48   \n",
       "3         05:13:31       morning               48   \n",
       "4         16:18:46       evening             2153   \n",
       "5         16:18:46       evening             2153   \n",
       "6         16:18:46       evening             2153   \n",
       "7         16:18:46       evening             2153   \n",
       "8         16:24:51       evening             2159   \n",
       "9         16:24:51       evening             2159   \n",
       "10        16:24:51       evening             2159   \n",
       "11        16:24:51       evening             2159   \n",
       "\n",
       "                                   diagnosisstring        icd9code  \\\n",
       "0     renal|disorder of kidney|acute renal failure    584.9, N17.9   \n",
       "1           oncology|chest tumors|breast CA|female  174.9, C50.919   \n",
       "2   cardiovascular|shock / hypotension|hypotension    458.9, I95.9   \n",
       "3         oncology|hematologic malignancy|leukemia                   \n",
       "4     renal|disorder of kidney|acute renal failure    584.9, N17.9   \n",
       "5   cardiovascular|shock / hypotension|hypotension    458.9, I95.9   \n",
       "6           oncology|chest tumors|breast CA|female  174.9, C50.919   \n",
       "7         oncology|hematologic malignancy|leukemia                   \n",
       "8   cardiovascular|shock / hypotension|hypotension    458.9, I95.9   \n",
       "9           oncology|chest tumors|breast CA|female  174.9, C50.919   \n",
       "10    renal|disorder of kidney|acute renal failure    584.9, N17.9   \n",
       "11        oncology|hematologic malignancy|leukemia                   \n",
       "\n",
       "   diagnosispriority  \n",
       "0              Major  \n",
       "1              Major  \n",
       "2            Primary  \n",
       "3              Major  \n",
       "4              Major  \n",
       "5            Primary  \n",
       "6              Major  \n",
       "7              Major  \n",
       "8            Primary  \n",
       "9              Major  \n",
       "10             Major  \n",
       "11             Major  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "select *\n",
    "from diagnosis\n",
    "where patientunitstayid = {}\n",
    "order by diagnosisoffset\n",
    "\"\"\".format(patientunitstayid)\n",
    "\n",
    "df = pd.read_sql_query(query, con)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that roughly on admission (`diagnosisoffset = 48` minutes after ICU admission), the patient had 4 problems documented:\n",
    "\n",
    "* acute renal failure (major condition)\n",
    "* breast cancer (major condition)\n",
    "* shock/hypotension (primary condition)\n",
    "* leukemia (major condition)\n",
    "\n",
    "Later in the stay (`diagnosisoffset = 2153`), the same conditions are redocumented, and then finally a few minutes later (`diagnosisoffset = 2159`) these conditions are documented a final time with `activeupondischarge = True`, indicating that the patient was discharged with these conditions.\n",
    "\n",
    "It's also worth noting that ICD-9 *and* ICD-10 codes are available with 3/4 of these diagnosis strings - only leukemia does not have matching codes. This often occurs because the `diagnosisstring` is at too high a level in the hierarchy, and so no appropriate ICD-9/ICD-10 code could be found. Let's look at this in more detail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ICD-9/ICD-10 code availability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosisstring</th>\n",
       "      <th>icd9code</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hematology|oncology and leukemia|carcinomatosis</td>\n",
       "      <td>199.0, C80.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hematology|oncology and leukemia|leukemia</td>\n",
       "      <td></td>\n",
       "      <td>79</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hematology|oncology and leukemia|leukemia|acut...</td>\n",
       "      <td>204.00, C91.00</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hematology|oncology and leukemia|leukemia|acut...</td>\n",
       "      <td>204.01, C91.01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hematology|oncology and leukemia|leukemia|acut...</td>\n",
       "      <td>205.00, C92.00</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>hematology|oncology and leukemia|leukemia|acut...</td>\n",
       "      <td>205.01, C92.01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>hematology|oncology and leukemia|leukemia|acut...</td>\n",
       "      <td>205.00, C92.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>hematology|oncology and leukemia|leukemia|acut...</td>\n",
       "      <td>205.00, C92.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>hematology|oncology and leukemia|leukemia|acut...</td>\n",
       "      <td>205.00, C92.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>hematology|oncology and leukemia|leukemia|chro...</td>\n",
       "      <td>204.10, C91.10</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     diagnosisstring        icd9code   n\n",
       "0    hematology|oncology and leukemia|carcinomatosis    199.0, C80.0   5\n",
       "1          hematology|oncology and leukemia|leukemia                  79\n",
       "2  hematology|oncology and leukemia|leukemia|acut...  204.00, C91.00  19\n",
       "3  hematology|oncology and leukemia|leukemia|acut...  204.01, C91.01   1\n",
       "4  hematology|oncology and leukemia|leukemia|acut...  205.00, C92.00  88\n",
       "5  hematology|oncology and leukemia|leukemia|acut...  205.01, C92.01   1\n",
       "6  hematology|oncology and leukemia|leukemia|acut...  205.00, C92.00   3\n",
       "7  hematology|oncology and leukemia|leukemia|acut...  205.00, C92.00   1\n",
       "8  hematology|oncology and leukemia|leukemia|acut...  205.00, C92.00   1\n",
       "9  hematology|oncology and leukemia|leukemia|chro...  204.10, C91.10  68"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dx = 'leukemia'\n",
    "query = query_schema + \"\"\"\n",
    "select diagnosisstring, icd9code\n",
    ", count(distinct patientunitstayid) as n\n",
    "from diagnosis\n",
    "where lower(diagnosisstring) like '%{}%'\n",
    "group by diagnosisstring, icd9code\n",
    "order by diagnosisstring, n desc\n",
    "\"\"\".format(dx)\n",
    "\n",
    "df = pd.read_sql_query(query, con)\n",
    "df.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, we can see that the high level 'hematology|oncology and leukemia|leukemia' does not have an associated ICD-9/ICD-10 code, but the more granular values of 'hematology|oncology and leukemia|leukemia|acute lymphocytic', 'hematology|oncology and leukemia|leukemia|in remission', etc do have mappings to ICD-9/ICD-10 codes. Thus, if using ICD codes to define diagnoses, it is highly recommended to first find associated problems, then identify levels higher up in the hierarchy in order to capture all patients."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying patients using ICD-9/ICD-10 codes\n",
    "\n",
    "Let's look at an example of finding patients using ICD-9/ICD-10 codes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosisstring</th>\n",
       "      <th>icd9code</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>endocrine|glucose metabolism|diabetes mellitus...</td>\n",
       "      <td>250.01, E10.9</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>endocrine|glucose metabolism|diabetes mellitus...</td>\n",
       "      <td>250.00, E11.9</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>endocrine|glucose metabolism|diabetes mellitus...</td>\n",
       "      <td>250.02, E11.65</td>\n",
       "      <td>791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>endocrine|glucose metabolism|diabetes mellitus...</td>\n",
       "      <td>250.03, E10.65</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>endocrine|glucose metabolism|DKA</td>\n",
       "      <td>250.13, E10.1</td>\n",
       "      <td>5024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>endocrine|glucose metabolism|DKA|with altered ...</td>\n",
       "      <td>250.33, E10.11</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>endocrine|glucose metabolism|hyperosmolar nonk...</td>\n",
       "      <td>250.23, E13.00</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>endocrine|glucose metabolism|hyperosmolar nonk...</td>\n",
       "      <td>250.22, E11.01</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     diagnosisstring        icd9code     n\n",
       "0  endocrine|glucose metabolism|diabetes mellitus...   250.01, E10.9    67\n",
       "1  endocrine|glucose metabolism|diabetes mellitus...   250.00, E11.9   678\n",
       "2  endocrine|glucose metabolism|diabetes mellitus...  250.02, E11.65   791\n",
       "3  endocrine|glucose metabolism|diabetes mellitus...  250.03, E10.65   211\n",
       "4                   endocrine|glucose metabolism|DKA   250.13, E10.1  5024\n",
       "5  endocrine|glucose metabolism|DKA|with altered ...  250.33, E10.11   333\n",
       "6  endocrine|glucose metabolism|hyperosmolar nonk...  250.23, E13.00   148\n",
       "7  endocrine|glucose metabolism|hyperosmolar nonk...  250.22, E11.01     5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icd9 = '250.'\n",
    "query = query_schema + \"\"\"\n",
    "select diagnosisstring, icd9code\n",
    ", count(distinct patientunitstayid) as n\n",
    "from diagnosis\n",
    "where icd9code like '%{}%'\n",
    "group by diagnosisstring, icd9code\n",
    "order by diagnosisstring, n desc\n",
    "\"\"\".format(icd9)\n",
    "\n",
    "df = pd.read_sql_query(query, con)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have identified 8 rows, beginning with 'endocrine|glucose metabolism'. Let's modify our query to search for this phrase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosisstring</th>\n",
       "      <th>icd9code</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>endocrine|glucose metabolism|diabetes mellitus</td>\n",
       "      <td></td>\n",
       "      <td>14620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>endocrine|glucose metabolism|diabetes mellitus...</td>\n",
       "      <td></td>\n",
       "      <td>497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>endocrine|glucose metabolism|diabetes mellitus...</td>\n",
       "      <td>250.01, E10.9</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>endocrine|glucose metabolism|diabetes mellitus...</td>\n",
       "      <td></td>\n",
       "      <td>3263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>endocrine|glucose metabolism|diabetes mellitus...</td>\n",
       "      <td>250.00, E11.9</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>endocrine|glucose metabolism|diabetes mellitus...</td>\n",
       "      <td>250.02, E11.65</td>\n",
       "      <td>791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>endocrine|glucose metabolism|diabetes mellitus...</td>\n",
       "      <td>250.03, E10.65</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>endocrine|glucose metabolism|DKA</td>\n",
       "      <td>250.13, E10.1</td>\n",
       "      <td>5024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>endocrine|glucose metabolism|DKA|with altered ...</td>\n",
       "      <td>250.33, E10.11</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>endocrine|glucose metabolism|hyperglycemia</td>\n",
       "      <td>790.6, R73.9</td>\n",
       "      <td>5485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>endocrine|glucose metabolism|hyperglycemia|cor...</td>\n",
       "      <td>790.6, R73.9</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>endocrine|glucose metabolism|hyperglycemia|due...</td>\n",
       "      <td>790.6, R73.9</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>endocrine|glucose metabolism|hyperglycemia|str...</td>\n",
       "      <td>790.6, R73.9</td>\n",
       "      <td>2483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>endocrine|glucose metabolism|hyperglycemia|sus...</td>\n",
       "      <td></td>\n",
       "      <td>1363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>endocrine|glucose metabolism|hyperosmolar nonk...</td>\n",
       "      <td></td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>endocrine|glucose metabolism|hyperosmolar nonk...</td>\n",
       "      <td>250.23, E13.00</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>endocrine|glucose metabolism|hyperosmolar nonk...</td>\n",
       "      <td>250.22, E11.01</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>endocrine|glucose metabolism|hypoglycemia</td>\n",
       "      <td>251.1, E16.2</td>\n",
       "      <td>1556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>endocrine|glucose metabolism|hypoglycemia|from...</td>\n",
       "      <td>251.1, E932.3, E16.0, T38.3</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>endocrine|glucose metabolism|hypoglycemia|from...</td>\n",
       "      <td>251.1, E16.1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>endocrine|glucose metabolism|hypoglycemia|from...</td>\n",
       "      <td>251.1, E932.3, E16.0, T38.3</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>endocrine|glucose metabolism|hypoglycemia|susp...</td>\n",
       "      <td></td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      diagnosisstring  \\\n",
       "0      endocrine|glucose metabolism|diabetes mellitus   \n",
       "1   endocrine|glucose metabolism|diabetes mellitus...   \n",
       "2   endocrine|glucose metabolism|diabetes mellitus...   \n",
       "3   endocrine|glucose metabolism|diabetes mellitus...   \n",
       "4   endocrine|glucose metabolism|diabetes mellitus...   \n",
       "5   endocrine|glucose metabolism|diabetes mellitus...   \n",
       "6   endocrine|glucose metabolism|diabetes mellitus...   \n",
       "7                    endocrine|glucose metabolism|DKA   \n",
       "8   endocrine|glucose metabolism|DKA|with altered ...   \n",
       "9          endocrine|glucose metabolism|hyperglycemia   \n",
       "10  endocrine|glucose metabolism|hyperglycemia|cor...   \n",
       "11  endocrine|glucose metabolism|hyperglycemia|due...   \n",
       "12  endocrine|glucose metabolism|hyperglycemia|str...   \n",
       "13  endocrine|glucose metabolism|hyperglycemia|sus...   \n",
       "14  endocrine|glucose metabolism|hyperosmolar nonk...   \n",
       "15  endocrine|glucose metabolism|hyperosmolar nonk...   \n",
       "16  endocrine|glucose metabolism|hyperosmolar nonk...   \n",
       "17          endocrine|glucose metabolism|hypoglycemia   \n",
       "18  endocrine|glucose metabolism|hypoglycemia|from...   \n",
       "19  endocrine|glucose metabolism|hypoglycemia|from...   \n",
       "20  endocrine|glucose metabolism|hypoglycemia|from...   \n",
       "21  endocrine|glucose metabolism|hypoglycemia|susp...   \n",
       "\n",
       "                       icd9code      n  \n",
       "0                                14620  \n",
       "1                                  497  \n",
       "2                 250.01, E10.9     67  \n",
       "3                                 3263  \n",
       "4                 250.00, E11.9    678  \n",
       "5                250.02, E11.65    791  \n",
       "6                250.03, E10.65    211  \n",
       "7                 250.13, E10.1   5024  \n",
       "8                250.33, E10.11    333  \n",
       "9                  790.6, R73.9   5485  \n",
       "10                 790.6, R73.9    395  \n",
       "11                 790.6, R73.9     22  \n",
       "12                 790.6, R73.9   2483  \n",
       "13                                1363  \n",
       "14                                 289  \n",
       "15               250.23, E13.00    148  \n",
       "16               250.22, E11.01      5  \n",
       "17                 251.1, E16.2   1556  \n",
       "18  251.1, E932.3, E16.0, T38.3     82  \n",
       "19                 251.1, E16.1      4  \n",
       "20  251.1, E932.3, E16.0, T38.3     43  \n",
       "21                                  60  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dx = 'endocrine|glucose metabolism'\n",
    "query = query_schema + \"\"\"\n",
    "select diagnosisstring, icd9code\n",
    ", count(distinct patientunitstayid) as n\n",
    "from diagnosis\n",
    "where diagnosisstring like '%{}%'\n",
    "group by diagnosisstring, icd9code\n",
    "order by diagnosisstring, n desc\n",
    "\"\"\".format(dx)\n",
    "\n",
    "df = pd.read_sql_query(query, con)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have 21 rows, and though not all are equivalent to diabetes, we do find that the top hit, 'endocrine|glucose metabolism|diabetes mellitus' has 14620 observations and is by far the most frequently documented problem for diagbetes, even though it does not have an ICD-9/ICD-10 code!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hospitals with data available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hospitalid</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_patients_with_tbl</th>\n",
       "      <th>data completion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>264</td>\n",
       "      <td>5237</td>\n",
       "      <td>4854</td>\n",
       "      <td>92.686653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>420</td>\n",
       "      <td>4679</td>\n",
       "      <td>4463</td>\n",
       "      <td>95.383629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>338</td>\n",
       "      <td>4277</td>\n",
       "      <td>4215</td>\n",
       "      <td>98.550386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>243</td>\n",
       "      <td>4243</td>\n",
       "      <td>4090</td>\n",
       "      <td>96.394061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>167</td>\n",
       "      <td>6092</td>\n",
       "      <td>3891</td>\n",
       "      <td>63.870650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>73</td>\n",
       "      <td>7059</td>\n",
       "      <td>3800</td>\n",
       "      <td>53.831988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>458</td>\n",
       "      <td>3701</td>\n",
       "      <td>3640</td>\n",
       "      <td>98.351797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>443</td>\n",
       "      <td>3656</td>\n",
       "      <td>3595</td>\n",
       "      <td>98.331510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>300</td>\n",
       "      <td>3617</td>\n",
       "      <td>3539</td>\n",
       "      <td>97.843517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>208</td>\n",
       "      <td>3650</td>\n",
       "      <td>3428</td>\n",
       "      <td>93.917808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     hospitalid  number_of_patients  number_of_patients_with_tbl  \\\n",
       "106         264                5237                         4854   \n",
       "184         420                4679                         4463   \n",
       "134         338                4277                         4215   \n",
       "90          243                4243                         4090   \n",
       "54          167                6092                         3891   \n",
       "11           73                7059                         3800   \n",
       "206         458                3701                         3640   \n",
       "200         443                3656                         3595   \n",
       "122         300                3617                         3539   \n",
       "80          208                3650                         3428   \n",
       "\n",
       "     data completion  \n",
       "106        92.686653  \n",
       "184        95.383629  \n",
       "134        98.550386  \n",
       "90         96.394061  \n",
       "54         63.870650  \n",
       "11         53.831988  \n",
       "206        98.351797  \n",
       "200        98.331510  \n",
       "122        97.843517  \n",
       "80         93.917808  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "select \n",
    "  pt.hospitalid\n",
    "  , count(distinct pt.patientunitstayid) as number_of_patients\n",
    "  , count(distinct dx.patientunitstayid) as number_of_patients_with_tbl\n",
    "from patient pt\n",
    "left join diagnosis dx\n",
    "  on pt.patientunitstayid = dx.patientunitstayid\n",
    "group by pt.hospitalid\n",
    "\"\"\".format(patientunitstayid)\n",
    "\n",
    "df = pd.read_sql_query(query, con)\n",
    "df['data completion'] = df['number_of_patients_with_tbl'] / df['number_of_patients'] * 100.0\n",
    "df.sort_values('number_of_patients_with_tbl', ascending=False, inplace=True)\n",
    "df.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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"
     },
     "metadata": {
      "jupyter-vega3": "#5fe6a275-9192-4bd7-905d-38a6941cb548"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['data completion']].vgplot.hist(bins=10,\n",
    "                                    var_name='Number of hospitals',\n",
    "                                    value_name='Percent of patients with data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The diagnosis table is a core component of eICU Care Manager, and the majority of hospitals use this interface routinely. However, the particular use of it may vary. For example, we can ask: how many hospitals document problems on ICU discharge?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hospitalid</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_patients_with_tbl</th>\n",
       "      <th>number_of_patients_with_active</th>\n",
       "      <th>data completion</th>\n",
       "      <th>active dx completion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>264</td>\n",
       "      <td>5237</td>\n",
       "      <td>4854</td>\n",
       "      <td>4627</td>\n",
       "      <td>92.686653</td>\n",
       "      <td>88.352110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>420</td>\n",
       "      <td>4679</td>\n",
       "      <td>4463</td>\n",
       "      <td>3968</td>\n",
       "      <td>95.383629</td>\n",
       "      <td>84.804445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>338</td>\n",
       "      <td>4277</td>\n",
       "      <td>4215</td>\n",
       "      <td>3651</td>\n",
       "      <td>98.550386</td>\n",
       "      <td>85.363573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>243</td>\n",
       "      <td>4243</td>\n",
       "      <td>4090</td>\n",
       "      <td>3263</td>\n",
       "      <td>96.394061</td>\n",
       "      <td>76.903135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>167</td>\n",
       "      <td>6092</td>\n",
       "      <td>3891</td>\n",
       "      <td>3193</td>\n",
       "      <td>63.870650</td>\n",
       "      <td>52.413001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     hospitalid  number_of_patients  number_of_patients_with_tbl  \\\n",
       "106         264                5237                         4854   \n",
       "184         420                4679                         4463   \n",
       "134         338                4277                         4215   \n",
       "90          243                4243                         4090   \n",
       "54          167                6092                         3891   \n",
       "\n",
       "     number_of_patients_with_active  data completion  active dx completion  \n",
       "106                            4627        92.686653             88.352110  \n",
       "184                            3968        95.383629             84.804445  \n",
       "134                            3651        98.550386             85.363573  \n",
       "90                             3263        96.394061             76.903135  \n",
       "54                             3193        63.870650             52.413001  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "select \n",
    "  pt.hospitalid\n",
    "  , count(distinct pt.patientunitstayid) as number_of_patients\n",
    "  , count(distinct dx.patientunitstayid) as number_of_patients_with_tbl\n",
    "  , count(distinct case when dx.activeupondischarge = 'True' then dx.patientunitstayid else null end)\n",
    "      as number_of_patients_with_active\n",
    "from patient pt\n",
    "left join diagnosis dx\n",
    "  on pt.patientunitstayid = dx.patientunitstayid\n",
    "group by pt.hospitalid\n",
    "\"\"\".format(patientunitstayid)\n",
    "\n",
    "df = pd.read_sql_query(query, con)\n",
    "df['data completion'] = df['number_of_patients_with_tbl'] / df['number_of_patients'] * 100.0\n",
    "df['active dx completion'] = df['number_of_patients_with_active'] / df['number_of_patients'] * 100.0\n",
    "df.sort_values('number_of_patients_with_tbl', ascending=False, inplace=True)\n",
    "df.head(n=10)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
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       ".vega-embed .vega-actions a {\n",
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       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega3": "#9dcf6f97-b57e-4120-93ee-decaa1aa4e82"
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    {
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"
     },
     "metadata": {
      "jupyter-vega3": "#9dcf6f97-b57e-4120-93ee-decaa1aa4e82"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['data completion', 'active dx completion']].vgplot.hist(bins=10, alpha=0.6, stacked=False,\n",
    "                                    var_name='Number of hospitals',\n",
    "                                    value_name='Percent of patients with data')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number is reduced, indicating there are a few ICUs which do not routinely document diagnosis on discharge. Conversely, for a few hospitals, every patient may have a diagnosis documented on discharge."
   ]
  }
 ],
 "metadata": {
  "front-matter": {
   "date": "2015-09-01",
   "linktitle": "admissiondrug",
   "title": "admissiondrug"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
